PathologyGAN: Learning Deep Representations of Cancer Tissue

Quiros, A. C. , Murray-Smith, R. and Yuan, K. (2020) PathologyGAN: Learning Deep Representations of Cancer Tissue. Proceedings of Machine Learning Research, 124, pp. 669-695.

Full text not currently available from Enlighten.

Publisher's URL: http://proceedings.mlr.press/v121/quiros20a.html

Abstract

We apply Generative Adversarial Networks (GANs) to the domain of digital pathology. Current machine learning research for digital pathology focuses on diagnosis, but we suggest a different approach and advocate that generative models could drive forward the understanding of morphological characteristics of cancer tissue. In this paper, we develop a framework which allows GANs to capture key tissue features and uses these characteristics to give structure to its latent space. To this end, we trained our model on 249K H+E breast cancer tissue images, extracted from 576 TMA images of patients from the Netherlands Cancer Institute (NKI) and Vancouver General Hospital (VGH) cohorts. We show that our model generates high quality images, with a Fréchet Inception Distance (FID) of 16.65. We further assess the quality of the images with cancer tissue characteristics (e.g. count of cancer, lymphocytes, or stromal cells), using quantitative information to calculate the FID and showing consistent performance of 9.86. Additionally, the latent space of our model shows an interpretable structure and allows semantic vector operations that translate into tissue feature transformations. Furthermore, ratings from two expert pathologists found no significant difference between our generated tissue images from real ones. The code, generated images, and pretrained model are available at \href{https://github.com/AdalbertoCq/Pathology-GAN}{https://github.com/AdalbertoCq/Pathology-GAN}

Item Type:Articles
Additional Information:Conference paper presented at International Conference on Medical Imaging with Deep Learning, Montréal, 6 ‑ 9 July 2020.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Yuan, Dr Ke and Murray-Smith, Professor Roderick
Authors: Quiros, A. C., Murray-Smith, R., and Yuan, K.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Proceedings of Machine Learning Research
Publisher:PMLR
ISSN:2640-3498
Copyright Holders:Copyright © 2020 A.C.Q. , R.M.-S. and K.Y
First Published:First published in Proceedings of Machine Learning Research 124:669-695
Publisher Policy:Reproduced under a Creative Commons license
Related URLs:

University Staff: Request a correction | Enlighten Editors: Update this record

Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
300982Exploiting Closed-Loop Aspects in Computationally and Data Intensive AnalyticsRoderick Murray-SmithEngineering and Physical Sciences Research Council (EPSRC)EP/R018634/1Computing Science
305567QuantIC - The UK Quantum Technoogy Hub in Quantum Enhanced ImagingMiles PadgettEngineering and Physical Sciences Research Council (EPSRC)EP/T00097X/1P&S - Physics & Astronomy
300982Exploiting Closed-Loop Aspects in Computationally and Data Intensive AnalyticsRoderick Murray-SmithEngineering and Physical Sciences Research Council (EPSRC)EP/R018634/1Computing Science